The Dr. Duck Medical Scanner system provides comprehensive medical analysis roleplay with animated sequences, visual scanning effects, and detailed diagnostic reports. Available in two versions: a sci-fi focused variant with randomized medical data, and an enhanced performance analysis version that scans real avatar attachments and provides actual performance metrics.
Both versions feature sit-based operation, T-pose and A-pose animation sequences, rotating scanner cylinders, cyan medical-themed particle effects, and atmospheric dialogue. Perfect for medical facilities, research stations, and sci-fi healthcare environments.
Pure roleplay focus with randomized medical data and futuristic diagnostics.
Combines roleplay with REAL avatar performance analysis using SL's object detection API.
| Feature | Sci-Fi Version | Performance Version |
|---|---|---|
| Basic Medical Report | ✅ Randomized vital signs | ✅ Randomized vital signs |
| Animation Sequence | ✅ T-pose → A-pose | ✅ T-pose → A-pose |
| Visual Effects | ✅ Full cylinder + particle FX | ✅ Full cylinder + particle FX |
| Attachment Scanning | ❌ Not included | ✅ REAL attachment detection |
| Avatar Complexity | ❌ Not measured | ✅ Calculated from attachments |
| Script Memory Analysis | ❌ Not measured | ✅ Total script KB reported |
| Performance Warnings | ❌ Not included | ✅ Threshold-based alerts |
| Device Manifest | ❌ Not included | ✅ Complete attachment list |
| Recommendations | Generic medical advice | Performance optimization tips |
| Best Use Case | Pure RP environments | RP + technical analysis |
The Performance Version scans actual avatar attachments using llGetAttachedList() and llGetObjectDetails() to provide genuine performance metrics!
┌─ PERFORMANCE ANALYSIS ────────────────┐
│ ✅ Avatar Complexity: EXCELLENT
│ 45,230 / 50,000 (optimal range)
│ ✅ Script Memory: EXCELLENT
│ 847 KB / 1,024 KB (optimal)
│ ✅ Attachment Count: GOOD
│ 18 / 25 attachments
│ ✅ HUD Count: GOOD
│ 3 HUD devices
└───────────────────────────────────────┘
The scanner provides contextual recommendations based on detected issues: